Variabilidade entre jogadores dentro do mesmo status posicional no vôlei masculino de alto nível
DOI:
https://doi.org/10.47197/retos.v46.93624Palavras-chave:
analise da performance, analise de jogo, variabilidade, desportos de equipa, padrões de jogoResumo
No desporto, pode haver vários jogadores para o mesmo estado posicional (por exemplo, no voleibol, há dois pontas, um perto do distribuidor e o outro longe do distribuidor), e pode haver diferenças relevantes dentro do mesmo estado posicional. Analisámos a variabilidade entre jogadores dentro do mesmo estado posicional no voleibol masculino de alto nível, através da Análise de Redes Sociais. As ações de ataque dos jogadores pontas perto (OHN) e afastado (OHA) do distribuidor foram analisadas em dez jogos das Finais da Liga das Nações de Voleibol de 2019 (278 jogadas). Foram criadas duas redes de centralidade de autovetor. Resultados: (a) no side-out, em condições não-ideais de distribuição, os OHNs preferiram um ataque forte, enquanto o OHA alternava entre ataque forte e amorti; (b) Após uma ação anterior, os OHNs atacaram através da exploração do bloco, enquanto o OHA preferia o amorti; (c) Após erros consecutivos, os OHNs jogam mais sobre o erro do adversário; (d) Após uma ação de defesa prévia, o OHN preferiu um ataque forte e a exploração do bloco adversário, enquanto a OHA preferia um ataque forte; (e) Em transição, o OHN sera solicitado com condições não-ideais de distribuição, enquanto o OHA era solicitado com condições ideais e não-ideais de distribuição. As nossas descobertas demonstram a variabilidade entre jogadores da mesma equipa e mesmo dentro do mesmo estatuto posicional. Isto permite que os treinadores compreendam as principais diferenças de jogadores com a mesma posição e, assim, atribuam melhor as sub-funções. Os investigadores devem ter cuidado ao agregar dados de jogadores de diferentes estatutos posicionais, e mesmo de jogadores dentro do mesmo estado posicional.
Referências
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Borgatti, S. P. (2005). Centrality and network flow. Social Networks, 27(1), 55–71. https://doi.org/10.1016/j.socnet.2004.11.008
Butterworth, A., O’Donoghue, P., & Cropley, B. (2013). Performance profiling in sports coaching: A review. International Journal of Performance Analysis in Sport, 13(3), 572–593. https://doi.org/10.1080/24748668.2013.11868672
Castelão, D. P., Garganta, J., Afonso, J., & Da Costa, I. T. (2015). Análise sequencial de comportamentos ofensivos desempenhados por seleções nacionais de futebol de alto rendimento. Revista Brasileira de Ciencias Do Esporte, 37(3), 230–236. https://doi.org/10.1016/j.rbce.2015.05.001
Clemente, F. M., Sarmento, H., & Aquino, R. (2020). Player position relationships with centrality in the passing network of world cup soccer teams: Win/loss match comparisons. Chaos, Solitons and Fractals, 133(109625). https://doi.org/10.1016/j.chaos.2020.109625
Costa, G., Afonso, J., Barbosa, R. V., Coutinho, P., & Mesquita, I. (2014). Predictors of attack efficacy and attack type in high-level brazilian women’s volleyball. Kinesiology, 46(2), 242–248.
Ferreira, A., Volossovitch, A., & Sampaio, J. (2014). Towards the game critical moments in basketball: A grounded theory approach. International Journal of Performance Analysis in Sport, 14(2), 428–442. https://doi.org/10.1080/24748668.2014.11868732
Fleiss, J., Levin, B., & Paik, M. C. (2013). Statistical methods for rates and proportions. Hoboken: John Wiley & Sons.
Gama, J., Passos, P., Davids, K., Relvas, H., Ribeiro, J., Vaz, V., & Dias, G. (2014). Network analysis and intra-team activity in attacking phases of professional football. International Journal of Performance Analysis in Sport, 14(3), 692–708. https://doi.org/10.1080/24748668.2014.11868752
Gonçalves, B. V., Figueira, B. E., Maçãs, V., & Sampaio, J. (2014). Effect of player position on movement behaviour, physical and physiological performances during an 11-a-side football game. Journal of Sports Sciences, 32(2), 191–199. https://doi.org/10.1080/02640414.2013.816761
Hughes, M., & Franks, I. (2008). The Essentials of Performance Analysis. In Routledge.
Hurst, M., Loureiro, M., Valongo, B., Laporta, L., Nikolaidis, T. P., & Afonso, J. (2016). Systemic Mapping of High-Level Women’s Volleyball using Social Network Analysis: The Case of Serve (K0), Side-out (KI), Side-out Transition (KII) and Transition (KIII). International Journal of Performance Analysis in Sport, 16(2), 695–710. https://doi.org/10.1080/24748668.2016.11868917
Laporta, L., Afonso, J., & Mesquita, I. (2018a). Interaction network analysis of the six game complexes in high-level volleyball through the use of Eigenvector Centrality. PLoS ONE, 13(9), 1–14. https://doi.org/10.1371/journal.pone.0203348
Laporta, L., Afonso, J., & Mesquita, I. (2018b). The need for weighting indirect connections between game variables: Social Network Analysis and eigenvector centrality applied to high-level men’s volleyball. International Journal of Performance Analysis in Sport, 18(6), 1067–1077. https://doi.org/10.1080/24748668.2018.1553094
Laporta, L., Afonso, J., Valongo, B., & Mesquita, I. (2019). Using social network analysis to assess play efficacy according to game patterns: a game-centred approach in high-level men’s volleyball. International Journal of Performance Analysis in Sport, 19(5), 866–877. https://doi.org/10.1080/24748668.2019.1669007
Laporta, L., Igor, A., Medeiros, A., Vargas, N., Conti, G. De, Costa, T., & Afonso, J. (2021). Coexistence of Distinct Performance Models in High-Level Women’s Volleyball. Journal of Human Kinetics, 78(April), 161–173. https://doi.org/10.2478/hukin-2021-0048
Lima, R., Palao, J. M., Moreira, M., & Clemente, F. M. (2019). Variations of technical actions and efficacy of national teams’ volleyball attackers according to their sex and playing positions. International Journal of Performance Analysis in Sport, 19(4), 491–502. https://doi.org/10.1080/24748668.2019.1625658
Liu, H., Gómez, M. A., Gonçalves, B., & Sampaio, J. (2016). Technical performance and match-to-match variation in elite football teams. Journal of Sports Sciences, 34(6), 509–518. https://doi.org/10.1080/02640414.2015.1117121
Marcelino, R., Mesquita, I., & Sampaio, J. (2011). Effects of quality of opposition and match status on technical and tactical performances in elite volleyball. Journal of Sports Sciences, 29(7), 733–741. https://doi.org/10.1080/02640414.2011.552516
Martins, J., Afonso, J., Mendes, A., Santos, L., & Mesquita, I. (2022). Inter-team variability in game play under critical game scenarios: a study in high-level men’s volleyball using social network analysis (Variabilidad entre equipos en el juego bajo escenarios críticos de juego: un estudio en voleibol masculino de alto ni. Retos, 43(1), 1095–1105. https://doi.org/10.47197/RETOS.V43I0.90505
Martins, J. B., Afonso, J., Coutinho, P., Fernandes, R., & Mesquita, I. (2021). The Attack in Volleyball from the Perspective of Social Network Analysis : Refining Match Analysis through Interconnectivity and Composite of Variables. Montenegrin Journal of Sports Science and Medicine, 10(1), 45–54. https://doi.org/10.26773/mjssm.210307
Mclean, S., Salmon, P. M., Gorman, A. D., Stevens, N. J., & Solomon, C. (2018). A social network analysis of the goal scoring passing networks of the 2016 European Football Championships. Human Movement Science, 57. https://doi.org/10.1016/j.humov.2017.10.001
Méndez, C., Gonçalves, B., Santos, J., Ribeiro, J. N., & Travassos, B. (2019). Attacking profiles of the best ranked teams from elite futsal leagues. Frontiers in Psychology, 10(1370), 403–414. https://doi.org/10.3389/fpsyg.2019.01370
Mesquita, I., Palao, J., Marcelino, R., & Afonso, J. (2013). Performance analysis in indoor volleyball and beach volleyball. In T. McGarry, P. O’Donoghue, & J. Sampaio (Eds.), Handbook of Sports Performance Analysis (pp. 367–379). London: Routledge.
Millán-Sánchez, A., Morante Rábago, J. C., & Ureña, A. (2017). Differences in the success of the attack between outside and opposite hitters in high level men’s volleyball. Journal of Human Sport and Exercise, 12(2), 251–256. https://doi.org/10.14198/jhse.2017.122.01
Moura, F. A., Santana, J. E., Vieira, N. A., Santiago, P. R. P., & Cunha, S. A. (2015). Analysis of Soccer Players’ Positional Variability during the 2012 UEFA European Championship: A Case Study. Journal of Human Kinetics, 47(1), 225–236. https://doi.org/10.1515/hukin-2015-0078
Project, D. (2019). Instruction Manual Data Volley 4. Bologna: Data Project.
Ribeiro, J., Silva, P., Duarte, R., Davids, K., & Garganta, J. (2017). Team Sports Performance Analysed Through the Lens of Social Network Theory: Implications for Research and Practice. Sports Medicine, 47(9), 1689–1696. https://doi.org/10.1007/s40279-017-0695-1
Stamm, R., Stamm, M., Torilo, D., Thomson, K., & Jairus, A. (2016). Comparative analysis of the elements of attack and defence in men’s and women’s games in the Estonian volleyball highest league. Papers on Anthropology, 25(1), 37. https://doi.org/10.12697/poa.2016.25.1.04
Tabachnick, B., & Fidell, L. (2007). Using multivariate statistics. Boston: Pearson.
Vargas, J., Loureiro, M., Nikolaidis, P. T., Knechtle, B., Laporta, L., Marcelino, R., & Afonso, J. (2018). Rethinking Monolithic Pathways to Success and Talent Identification: The Case of the Women’s Japanese Volleyball Team and Why Height is Not Everything. Journal of Human Kinetics, 64(1), 233–245. https://doi.org/10.1515/hukin-2017-0197
Wäsche, H., Dickson, G., Woll, A., & Brandes, U. (2017). Social network analysis in sport research: an emerging paradigm. European Journal for Sport and Society, 14(2), 138–165. https://doi.org/10.1080/16138171.2017.1318198
Yi, Q., Gómez, M. Á., Liu, H., & Sampaio, J. (2019). Variation of match statistics and football teams’ match performance in the group stage of the UEFA champions league from 2010 to 2017. Kinesiology, 51(2), 170–181. https://doi.org/10.26582/k.51.2.4
Bonacich, P. (2007). Some unique properties of eigenvector centrality. Social Networks, 29(4), 555–564. https://doi.org/10.1016/j.socnet.2007.04.002
Borgatti, S. P. (2005). Centrality and network flow. Social Networks, 27(1), 55–71. https://doi.org/10.1016/j.socnet.2004.11.008
Butterworth, A., O’Donoghue, P., & Cropley, B. (2013). Performance profiling in sports coaching: A review. International Journal of Performance Analysis in Sport, 13(3), 572–593. https://doi.org/10.1080/24748668.2013.11868672
Castelão, D. P., Garganta, J., Afonso, J., & Da Costa, I. T. (2015). Análise sequencial de comportamentos ofensivos desempenhados por seleções nacionais de futebol de alto rendimento. Revista Brasileira de Ciencias Do Esporte, 37(3), 230–236. https://doi.org/10.1016/j.rbce.2015.05.001
Clemente, F. M., Sarmento, H., & Aquino, R. (2020). Player position relationships with centrality in the passing network of world cup soccer teams: Win/loss match comparisons. Chaos, Solitons and Fractals, 133(109625). https://doi.org/10.1016/j.chaos.2020.109625
Costa, G., Afonso, J., Barbosa, R. V., Coutinho, P., & Mesquita, I. (2014). Predictors of attack efficacy and attack type in high-level brazilian women’s volleyball. Kinesiology, 46(2), 242–248.
Ferreira, A., Volossovitch, A., & Sampaio, J. (2014). Towards the game critical moments in basketball: A grounded theory approach. International Journal of Performance Analysis in Sport, 14(2), 428–442. https://doi.org/10.1080/24748668.2014.11868732
Fleiss, J., Levin, B., & Paik, M. C. (2013). Statistical methods for rates and proportions. Hoboken: John Wiley & Sons.
Gama, J., Passos, P., Davids, K., Relvas, H., Ribeiro, J., Vaz, V., & Dias, G. (2014). Network analysis and intra-team activity in attacking phases of professional football. International Journal of Performance Analysis in Sport, 14(3), 692–708. https://doi.org/10.1080/24748668.2014.11868752
Gonçalves, B. V., Figueira, B. E., Maçãs, V., & Sampaio, J. (2014). Effect of player position on movement behaviour, physical and physiological performances during an 11-a-side football game. Journal of Sports Sciences, 32(2), 191–199. https://doi.org/10.1080/02640414.2013.816761
Hughes, M., & Franks, I. (2008). The Essentials of Performance Analysis. In Routledge.
Hurst, M., Loureiro, M., Valongo, B., Laporta, L., Nikolaidis, T. P., & Afonso, J. (2016). Systemic Mapping of High-Level Women’s Volleyball using Social Network Analysis: The Case of Serve (K0), Side-out (KI), Side-out Transition (KII) and Transition (KIII). International Journal of Performance Analysis in Sport, 16(2), 695–710. https://doi.org/10.1080/24748668.2016.11868917
Laporta, L., Afonso, J., & Mesquita, I. (2018a). Interaction network analysis of the six game complexes in high-level volleyball through the use of Eigenvector Centrality. PLoS ONE, 13(9), 1–14. https://doi.org/10.1371/journal.pone.0203348
Laporta, L., Afonso, J., & Mesquita, I. (2018b). The need for weighting indirect connections between game variables: Social Network Analysis and eigenvector centrality applied to high-level men’s volleyball. International Journal of Performance Analysis in Sport, 18(6), 1067–1077. https://doi.org/10.1080/24748668.2018.1553094
Laporta, L., Afonso, J., Valongo, B., & Mesquita, I. (2019). Using social network analysis to assess play efficacy according to game patterns: a game-centred approach in high-level men’s volleyball. International Journal of Performance Analysis in Sport, 19(5), 866–877. https://doi.org/10.1080/24748668.2019.1669007
Laporta, L., Igor, A., Medeiros, A., Vargas, N., Conti, G. De, Costa, T., & Afonso, J. (2021). Coexistence of Distinct Performance Models in High-Level Women’s Volleyball. Journal of Human Kinetics, 78(April), 161–173. https://doi.org/10.2478/hukin-2021-0048
Lima, R., Palao, J. M., Moreira, M., & Clemente, F. M. (2019). Variations of technical actions and efficacy of national teams’ volleyball attackers according to their sex and playing positions. International Journal of Performance Analysis in Sport, 19(4), 491–502. https://doi.org/10.1080/24748668.2019.1625658
Liu, H., Gómez, M. A., Gonçalves, B., & Sampaio, J. (2016). Technical performance and match-to-match variation in elite football teams. Journal of Sports Sciences, 34(6), 509–518. https://doi.org/10.1080/02640414.2015.1117121
Marcelino, R., Mesquita, I., & Sampaio, J. (2011). Effects of quality of opposition and match status on technical and tactical performances in elite volleyball. Journal of Sports Sciences, 29(7), 733–741. https://doi.org/10.1080/02640414.2011.552516
Martins, J., Afonso, J., Mendes, A., Santos, L., & Mesquita, I. (2022). Inter-team variability in game play under critical game scenarios: a study in high-level men’s volleyball using social network analysis (Variabilidad entre equipos en el juego bajo escenarios críticos de juego: un estudio en voleibol masculino de alto ni. Retos, 43(1), 1095–1105. https://doi.org/10.47197/RETOS.V43I0.90505
Martins, J. B., Afonso, J., Coutinho, P., Fernandes, R., & Mesquita, I. (2021). The Attack in Volleyball from the Perspective of Social Network Analysis : Refining Match Analysis through Interconnectivity and Composite of Variables. Montenegrin Journal of Sports Science and Medicine, 10(1), 45–54. https://doi.org/10.26773/mjssm.210307
Mclean, S., Salmon, P. M., Gorman, A. D., Stevens, N. J., & Solomon, C. (2018). A social network analysis of the goal scoring passing networks of the 2016 European Football Championships. Human Movement Science, 57. https://doi.org/10.1016/j.humov.2017.10.001
Méndez, C., Gonçalves, B., Santos, J., Ribeiro, J. N., & Travassos, B. (2019). Attacking profiles of the best ranked teams from elite futsal leagues. Frontiers in Psychology, 10(1370), 403–414. https://doi.org/10.3389/fpsyg.2019.01370
Mesquita, I., Palao, J., Marcelino, R., & Afonso, J. (2013). Performance analysis in indoor volleyball and beach volleyball. In T. McGarry, P. O’Donoghue, & J. Sampaio (Eds.), Handbook of Sports Performance Analysis (pp. 367–379). London: Routledge.
Millán-Sánchez, A., Morante Rábago, J. C., & Ureña, A. (2017). Differences in the success of the attack between outside and opposite hitters in high level men’s volleyball. Journal of Human Sport and Exercise, 12(2), 251–256. https://doi.org/10.14198/jhse.2017.122.01
Moura, F. A., Santana, J. E., Vieira, N. A., Santiago, P. R. P., & Cunha, S. A. (2015). Analysis of Soccer Players’ Positional Variability during the 2012 UEFA European Championship: A Case Study. Journal of Human Kinetics, 47(1), 225–236. https://doi.org/10.1515/hukin-2015-0078
Project, D. (2019). Instruction Manual Data Volley 4. Bologna: Data Project.
Ribeiro, J., Silva, P., Duarte, R., Davids, K., & Garganta, J. (2017). Team Sports Performance Analysed Through the Lens of Social Network Theory: Implications for Research and Practice. Sports Medicine, 47(9), 1689–1696. https://doi.org/10.1007/s40279-017-0695-1
Stamm, R., Stamm, M., Torilo, D., Thomson, K., & Jairus, A. (2016). Comparative analysis of the elements of attack and defence in men’s and women’s games in the Estonian volleyball highest league. Papers on Anthropology, 25(1), 37. https://doi.org/10.12697/poa.2016.25.1.04
Tabachnick, B., & Fidell, L. (2007). Using multivariate statistics. Boston: Pearson.
Vargas, J., Loureiro, M., Nikolaidis, P. T., Knechtle, B., Laporta, L., Marcelino, R., & Afonso, J. (2018). Rethinking Monolithic Pathways to Success and Talent Identification: The Case of the Women’s Japanese Volleyball Team and Why Height is Not Everything. Journal of Human Kinetics, 64(1), 233–245. https://doi.org/10.1515/hukin-2017-0197
Wäsche, H., Dickson, G., Woll, A., & Brandes, U. (2017). Social network analysis in sport research: an emerging paradigm. European Journal for Sport and Society, 14(2), 138–165. https://doi.org/10.1080/16138171.2017.1318198
Yi, Q., Gómez, M. Á., Liu, H., & Sampaio, J. (2019). Variation of match statistics and football teams’ match performance in the group stage of the UEFA champions league from 2010 to 2017. Kinesiology, 51(2), 170–181. https://doi.org/10.26582/k.51.2.4
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2022-09-28
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Martins, J. B., Afonso, J., Mendes, A., Santos, L., & Mesquita, I. (2022). Variabilidade entre jogadores dentro do mesmo status posicional no vôlei masculino de alto nível. Retos, 46, 129–137. https://doi.org/10.47197/retos.v46.93624
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